Anthropic's Claude Code Gains Traction

Want local vibe coding? This AI stack replaces Claude Code and Codex - and it's free #Technology #SoftwareandApps #Other #AI #Coding #TechInnovation You can replace expensive, cloud-based AI coding with three tools. Here's how.

AI's Coding Revolution May Spark a Developer Shortage, Argues Daniel Priestley In a recent LinkedIn post, Daniel Priestley explores the paradoxical possibility that AI's increasing ability to code could lead to a global shortage of huma…

AI's Coding Revolution May Spark a Developer Shortage, Argues Daniel Priestley In a recent LinkedIn post, Daniel Priestley explores the paradoxical possibility that AI's increasing ability to code could lead to a global shortage of huma…

How AI coding makes developers 56% faster and 19% slower... a quick explanation of how reasonable research can arrive at such different conclusions (and how you should respond!) On The New Stack: https://zurl.co/gQ5kM

Anthropic CEO: "90% Of Coding Could Be Done by AI In 3-6 Months, 100% Possible in a Year: Anthropic CEO" 😳 Dario Amodei just dropped this prediction, and honestly? My first reaction was panic too. But then I stepped back and realized something... Remember when we thought Google would make us dumb? Or Stack Overflow would replace actually learning to code? Or when frameworks were "cheating"? We adapted. We got better. And honestly, we became way more productive. Here's what I'm doing (and maybe it'll help you too): 1. I stopped fighting it I started using Claude and Copilot daily. Not gonna lie—it's pretty amazing when AI handles the boring stuff while I focus on the interesting problems. Why write boilerplate when I could be architecting solutions? 2. I'm getting better at asking questions Turns out, working with AI is like working with a really fast junior developer. You need to know WHAT you want, WHY you want it, and HOW to review it. That requires deeper understanding, not less. 3. I'm investing in skills AI can't touch (yet) Understanding business problems and translating them to tech System design and making tough architectural choices Debugging AI's mistakes (trust me, it makes plenty) Security and code review Mentoring and collaboration 4. I'm learning faster than ever With AI handling syntax, I'm exploring new languages and frameworks way quicker. Started playing with Rust last week—something I'd been putting off for months. The honest truth? Yes, entry-level coding is changing. But here's what people miss: good developers were NEVER just code monkeys. We're problem solvers who happen to use code as a tool. AI is just giving us a better tool. What actually worries me: Not AI replacing developers. I worry about developers who refuse to adapt. The ones who say "I'll never use AI" while the industry moves forward. What excites me: Building products that would've taken months in just weeks. Having time to experiment. Actually enjoying coding again instead of drowning in boilerplate. Real talk for juniors: Don't let this scare you. You're actually entering at the BEST time. You won't waste years learning syntax—you'll start solving real problems from day one. Just make sure you understand the fundamentals and learn to work alongside AI. For senior devs: We're not dinosaurs. We're becoming architects, reviewers, and strategic thinkers. The boring parts are being automated. The interesting parts? Still ours. Look, I don't know if Amodei's timeline is right. Maybe it's 6 months, maybe it's 3 years. But I do know this: the developers who survive aren't the ones who code the fastest. They're the ones who solve problems the best. And AI? It's just another tool in our toolkit. What are you doing to adapt? Genuinely curious to hear your thoughts—especially if you disagree with me. Drop a comment 👇 #RealTalk #DeveloperLife #AIandCoding #CareerAdvice #TechIndustry #LearningInPublic

How are AI coding tools actually changing the day-to-day for engineers? We surveyed over 1,100 professional developers for our latest State of Code Developer Survey. We looked into the efficiencies, the frustrations, and the new workflows emerging on the ground. The data reveals a fundamental shift: - High adoption: 72% of developers who have tried AI coding tools now use them every day. - Significant output: 42% of code currently being committed is AI-generated or assisted. This represents a transition into a new era where human and AI agents work in synergy. But as code production accelerates, a new challenge has emerged—the verification bottleneck. While developers are using AI to "vibe" and create at an unprecedented pace, the need to verify that code for quality and security is more critical than ever. How is your team's workflow changing? Are you seeing that 42% mark in your own repositories? Learn more: https://lnkd.in/ga5uQeqg #AI #StateOfCode #DeveloperSurvey

This is an excellent take on vibe-coding (which I still think is amazing and you should definitely try). “With “junk” (or “dark”) flow we lose our ability to accurately assess our productivity levels and the quality of our work. A study from METR found that when developers used AI tools, they estimated that they were working 20% faster, yet in reality they worked 19% slower. That is nearly a 40% difference between perceived and actual times!” The problem with vibe-coding is that you can start coding without clear abstractions on what to build in your head. If you are aware of this (like when building something with new technology you never tried) it’s amazing since you can build software with tools you never knew existed but sometimes you end up making a mess. Also you might learn nothing which isn’t great long term. At the same time. I think vibe coding is really helpful in the same way AI writing is helpful, to help you scaffold out your ideas by growing your abstraction through AI code / writing on a topic. Underlying the issues with vibe coding is one of the biggest AI risk (according to me) sycophancy, the fact that AI models will entertain your delusions and sometimes encourage them . You can spend significant time building out a bad idea with AI, since you can build it without talking to anyone. https://lnkd.in/dqgZ589s

A really interesting read from Benj Edwards over on Ars Technica about the future of coding (and CS) in the world of AI and Vibe Coding. Benj Edwards interviewed leaders in coding & cs fields to gain their insight. Thank you to Benj Edwards for asking great questions and getting really interesting insight. I particularly like the insight about education from Roland Dreier: "Dreier, while feeling secure in his own position, worried about the path for newcomers. 'There are going to have to be changes to education and training to get junior developers the experience and judgment they need,' he said, 'when it’s just a waste to make them implement small pieces of a system like I came up doing.' " I believe we should be teaching our students (particularly in higher education) how to use these tools as a force multiplier to make huge projects which demonstrate their subject knowledge and their ability to use AI in their work. https://lnkd.in/gbHuMimR #AI #VibeCoding #ComputerScience #CS #HigherEducation

Top five pros and cons of AI agentic coding... Yes, I've devolved into writing listicles. Regardless, these thoughts and themes have been running rampant in my brain, and this will help purge them. So indulge me, for a moment. The pros... 5: Release from syntax burdens. This is a huge win. I prefer C#, and with the changes that come every year, I don't always know what the most elegant way to write something is. Also, I often forget how to use delegates and events, but the machine can get it right the first time. 4: Import expertise that you don't have. First time needing to use that library, or even that language? The AI's got you covered. It even works figuring out build YAML and such. 3: Pairing partner that works for cheap. Pairing interactions go pretty well, you just have to keep prompting it to get where you know it has to be. It's like working with a junior developer, driving at the keyboard while you discuss things. 2: No documentation hunting. Now I don't have to go find the docs, I can just ask the robot. 1: Types faster than you can. This is especially true when you're working on something solo, and not on a team or in an enterprise. You know what good looks like, and you can describe it, go get an energy drink, and come back to exactly what you wanted. The cons... 5: You have to trust and verify. I don't think this avoidable. You have to check the robot's work to make sure it isn't doing things that are security risks, or performance issues-in-waiting. It makes these mistakes a lot. 4: Coding isn't your biggest problem in the first place. I've been harping on this for weeks. It can generate code fast, yes, but coding by many estimates is 10-20% of the time spent in product development. The creativity and product requirements are the genesis of innovation. AI can't do that. The 80/20 problem still exists. 3: Trained on crappy code. Let's be realistic, most code in the wild isn't great, and that's what the robots trained on. So it's unsurprising when the machine comes up with some ugly code smells, like a nullable boolean. Why does it believe that a boolean with three states is a good idea? 2: Overconfidence. While this is better than it was a year ago, I'm surprised when it generates something that won't even build. When you tell it what to fix, it high fives you and congratulates you on being right. But it was so sure in the first place! Now put this in the hands of vibers, like lawyers or product managers, who don't know what to look for, and you've got trouble. 1: AI lacks context. It doesn't know what it doesn't know. For example, if a user should match the owner of a record fetched by an API, it doesn't know to check that unless you tell it to. Looping back to #4, so much time is spent describing the outcomes that you're after, whether you're a human developer or a machine.